International Journal of Research and Scientific Innovation (IJRSI)
Stress Detection Using Machine Learning Algorithms
Published April 29, 2026 • Vol. 13, Issue 4, pp. 623–633Open Access
DOI: 10.51244/IJRSI.2026.1304000062
Abstract
Stress management is becoming more and more crucial in today's fast-paced technological environment, particularly for IT professionals. Long working hours, strict deadlines and high expectations are common aspects of the work environment in the IT sector, and these can raise stress levels. Unmanaged stress has an adverse effect on professionals' health and well-being as well as their productivity and job happiness. A data set comprising of 2343 sample values taken from Kaggle is used for detecting the stress levels
Keywords: Stress detection, Machine learning, Deep Neural Networks
| Journal | International Journal of Research and Scientific Innovation (IJRSI) |
|---|---|
| ISSN | 2321-2705 |
| Volume / Issue | Volume 13, Issue 4 |
| Pages | 623–633 |
| Publication date | April 29, 2026 |
| DOI | 10.51244/IJRSI.2026.1304000062 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Dr Usha Kamale (2026). Stress Detection Using Machine Learning Algorithms. International Journal of Research and Scientific Innovation (IJRSI), 13(4), 623-633. https://doi.org/10.51244/IJRSI.2026.1304000062
BibTeX
@article{Dr2026,
title = {Stress Detection Using Machine Learning Algorithms},
author = {Dr Usha Kamale},
journal = {International Journal of Research and Scientific Innovation (IJRSI)},
volume = {13},
number = {4},
pages = {623--633},
year = {2026},
doi = {10.51244/IJRSI.2026.1304000062},
publisher = {RSIS International}
}